A swarm intelligence based coordination algorithm for distributed multi-agent systems

被引：3

作者：

Meng, Yan ^{[1
]}

Kazeem, Olorundamilola ^{[1
]}

Muller, Juan C. ^{[2
]}

机构：

[1] Stevens Inst Technol, Dept Elect & Comp Engn, Hoboken, NJ 07030 USA

[2] New Jersey City Univ, Dept Comp Sci & Math, Jersey, NJ USA

来源：

2007 INTERNATIONAL CONFERENCE ON INTEGRATION OF KNOWLEDGE INTENSIVE MULTI-AGENT SYSTEMS | 2007年

基金：

美国国家科学基金会;

关键词：

D O I：

10.1109/KIMAS.2007.369825

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a synergy of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) into a novel hybrid coordination algorithm for distributed multi-agent systems. The intended multi-agent systems are composed of relatively simple, expendable agents with highly decentralized, self-organized behaviors; which as a whole achieve global optimization over a set task. Basically, two coordination processes among the agents will be established. One is a stigmergy-based algorithm using the distributed virtual pheromones to guide the agents' movement, the other one is interaction-based algorithm, where a global maximum of the attribute values can be obtained through the interaction between the agents. The simulation results demonstrate that the proposed hybrid swarm intelligence based architecture is feasible, efficient, and robust to coordinate a simulated distributed multi-agent system.

引用

页码：294 / +

页数：2

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